Towards Privacy Preserving Data Publishing in Inter Cloud Infrastructure
Towards Privacy Preserving Data Publishing in Inter Cloud Infrastructure |
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© 2022 by IJETT Journal | ||
Volume-70 Issue-10 |
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Year of Publication : 2022 | ||
Authors : Veena Gadad, C. N. Sowmyarani |
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DOI : 10.14445/22315381/IJETT-V70I10P204 |
How to Cite?
Veena Gadad, C. N. Sowmyarani, "Towards Privacy Preserving Data Publishing in Inter Cloud Infrastructure," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 27-34, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P204
Abstract
Data privacy is a prime concern in this digital era since an enormous amount of data is collected, stored and
published regularly. Due to gratifying features like data sharing, easy maintenance, economical, large network access and
fast processing, many organizations and users leverage the cloud environment for data storage and access. However, when
such an environment is used for data publishing, there are chances of an individual’s identity and sensitive information
leakage. These are caused by the external attacker and the internal cloud environment. Privacy Preserving Data Publishing
(PPDP) is a suite of anonymization algorithms that aim to prevent such attacks while simultaneously safeguarding the
person's identity. Studies have shown that popular privacy algorithms like p sensitive k-anonymity, KP cover and
differential privacy, though they provide stronger privacy, are less efficient in preventing emerging attacks. This paper
proposes a novel algorithm to publish data in the public cloud and prove that it is computationally efficient and prevents
privacy attacks that are especially caused by the data published in the cloud environment.
Keywords
Data Privacy, Privacy attacks, anonymization, PPDP, Differential Privacy, Cloud data privacy.
Reference
[1] P. Mell, T. Grance, and Others, ‘the Nist Definition of Cloud Computing’, 2011.
[2] “Beyond Gdpr: Data Protection Around the World,” Thales Group, 10-May-2021.[Online].Available:
https://www.Thalesgroup.Com/EN/Markets/Digital-Identity-and-Security/Government/Magazine/Beyond-Gdpr-Data-ProtectionAround-World. [Accessed: 06-May-2022].
[3] J. Domingo-Ferrer, O. Farràs, J. Ribes-González, and D. Sánchez, “Privacy-Preserving Cloud Computing on Sensitive Data: A Survey
of Methods, Products and Challenges,” Computer Communications, . Elsevier Bv, vol. 140–141Pp. 38–60, May 2019. Doi:
10.1016/J.Comcom.2019.04.011.
[4] P. K. P, S. K. P, and A. P.J.A., “Attribute Based Encryption in Cloud Computing: A Survey, Gap Analysis, and Future Directions,”
Journal of Network and Computer Applications, vol. 108. Elsevier Bv, pp. 37–52, 2018. Doi: 10.1016/J.Jnca.2018.02.009.
[5] N. Kaaniche and M. Laurent, “Data Security and Privacy Preservation in Cloud Storage Environments Based on Cryptographic
Mechanisms,” Computer Communications,” Elsevier Bv, vol. 111, pp. 120–141, 2017. Doi: 10.1016/J.Comcom.2017.07.006.
[6] Sowmyarani C. N. and Dayananda P, “Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing,” Security
Solutions and Applied Cryptography in Smart Grid Communications. IGI Global, pp. 98–116. Doi: 10.4018/978-1-5225-1829-
7.Ch006
[7] X. Xiao Και Y. Tao, “Anatomy: Simple and Effective Privacy Preservation,” Proceedings of the 32nd International Conference on
Very Large Data Bases, pp. 139–150, 2006.
[8] P. Samarati Κ, L. Sweeney, “Protecting Privacy When Disclosing Information: K-Anonymity and Its Enforcement Through
Generalization and Suppression,” 1998.
[9] K. Lefevre, D. J. Dewitt, Και R. Ramakrishnan, “Incognito: Efficient Full-Domain K-Anonymity,” Proceedings of the 2005 ACM
Sigmod International Conference on Management of Data, pp. 49–60, 2005.
[10] C. C. Aggarwal, “On K-Anonymity and the Curse of Dimensionality,” ΣΤΟ VLDB, 2005, vol. 5, pp. 901–909.
[11] T. Li, N. Li, J. Zhang, Κ, I. Molloy, “Slicing: A New Approach for Privacy-Preserving Data Publishing,” IEEE Transactions on
Knowledge and Data Engineering, vol. 24, pp. 561–574, 2010.
[12] M. Wang, Z. Jiang, Y. Zhang, Και H. Yang, “T-Closeness Slicing: A New Privacy-Preserving Approach for Transactional Data
Publishing,” INFORMS Journal on Computing, vol. 30, pp. 438–453, 2018.
[13] Andrea Li, "Privacy, Security and Trust Issues in Cloud Computing," SSRG International Journal of Computer Science and
Engineering, vol. 6, no. 10, pp. 29-32, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I10P106.
[14] Y. Tao, H. Chen, X. Xiao, S. Zhou, Και D. Zhang, “Angel: Enhancing the Utility of Generalization for Privacy-Preserving
Publication,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, 7, pp. 1073–1087, 2009.
[15] Q. Zhang, N. Koudas, D. Srivastava, T. Yu, “Aggregate Query Answering on Anonymized Tables,” 2007 IEEE 23rd International
Conference on Data Engineering, pp. 116–125, 2007.
[16] D. Li, X. He, L. Cao, Και H. Chen, “Permutation Anonymization,” Journal of Intelligent Information Systems, vol. 47, no. 3, pp.
427–445, 2016.
[17] M. Bahrami Και M. Singhal, “A Lightweight Permutation Based Method for Data Privacy in Mobile Cloud Computing,” 2015 3rd
IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 189–198, 2015.
[18] M. Bahrami, D. Li, M. Singhal, Και A. Kundu, “An Efficient Parallel Implementation of A Lightweight Data Privacy Method for
Mobile Cloud Users,” 2016 Seventh International Workshop on Data-Intensive Computing in the Clouds (Datacloud), pp. 51–58,
2016.
[19] Maryann Thomas, S. V. Athawale, "Study of Cloud Computing Security Methods: Cryptography," SSRG International Journal of
Computer Science and Engineering, vol. 6, no. 4, pp. 1-5, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I4P101.
[20] S. E. Fienberg Και J. Mcintyre, “Data Swapping: Variations on A Theme By Dalenius and REISS,” Privacy in Statistical Databases,
Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 14–29, 2004.
[21] J. Domingo-Ferrer Κ, J. M. Mateo-Sanz, “Practical Data-Oriented Microaggregation for Statistical Disclosure, “ IEEE Transactions
on Knowledge and Data Engineering, vol. 14, 1, pp. 189–201, 2002.
[22] H. Jian-Min, C. Ting-Ting, Κ, Y. Hui-Qun, “An Improved V-Mdav Algorithm for L-Diversity,” 2008 International Symposiums on
Information Processing, pp. 733–739, 2008.
[23] C. N. Sowmyarani, V. Gadad, Κ, P. Dayananda, “(P+, Α, T)-Anonymity Technique Against Privacy Attacks,” International Journal
of Information Security and Privacy (IJISP), vol. 15, no. 2, pp. 68–86, 2021.
[24] K. Lefevre, D. J. Dewitt, R. Ramakrishnan, “Mondrian Multidimensional K-Anonymity,” 22nd International Conference on Data
Engineering (ICDE’06), pp. 25–25, 2006.
[25] C. Blake, “Uci Repository of Machine Learning Databases,” http://www. ICS. Uci. Edu/\ Mlearn/Mlrepository. html, 1998.
[26] V. S. Susan,T. Christopher, “Anatomisation With Slicing: A New Privacy Preservation Approach for Multiple Sensitive Attributes,”
Springerplus, vol. 5, pp. 1–21, 2016.
[27] C. Dwork, “Differential Privacy: A Survey of Results,” International Conference on Theory and Applications of Models of
Computation, pp. 1–19, 2008.